6,191 research outputs found
Performance Analysis of l_0 Norm Constraint Least Mean Square Algorithm
As one of the recently proposed algorithms for sparse system identification,
norm constraint Least Mean Square (-LMS) algorithm modifies the cost
function of the traditional method with a penalty of tap-weight sparsity. The
performance of -LMS is quite attractive compared with its various
precursors. However, there has been no detailed study of its performance. This
paper presents all-around and throughout theoretical performance analysis of
-LMS for white Gaussian input data based on some reasonable assumptions.
Expressions for steady-state mean square deviation (MSD) are derived and
discussed with respect to algorithm parameters and system sparsity. The
parameter selection rule is established for achieving the best performance.
Approximated with Taylor series, the instantaneous behavior is also derived. In
addition, the relationship between -LMS and some previous arts and the
sufficient conditions for -LMS to accelerate convergence are set up.
Finally, all of the theoretical results are compared with simulations and are
shown to agree well in a large range of parameter setting.Comment: 31 pages, 8 figure
A Robust Zero-point Attraction LMS Algorithm on Near Sparse System Identification
The newly proposed norm constraint zero-point attraction Least Mean
Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse
system identification. However, ZA-LMS has less advantage against standard LMS
when the system is near sparse. Thus, in this paper, firstly the near sparse
system modeling by Generalized Gaussian Distribution is recommended, where the
sparsity is defined accordingly. Secondly, two modifications to the ZA-LMS
algorithm have been made. The norm penalty is replaced by a partial
norm in the cost function, enhancing robustness without increasing the
computational complexity. Moreover, the zero-point attraction item is weighted
by the magnitude of estimation error which adjusts the zero-point attraction
force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS
(DWZA-LMS) algorithm is further proposed, which shows better performance on
near sparse system identification. In addition, the mean square performance of
DWZA-LMS algorithm is analyzed. Finally, computer simulations demonstrate the
effectiveness of the proposed algorithm and verify the result of theoretical
analysis.Comment: 20 pages, 11 figure
Spectral function and fidelity susceptibility in quantum critical phenomena
In this paper, we derive a simple equality that relates the spectral function
and the fidelity susceptibility , i.e. with being
the half-width of the resonance peak in the spectral function. Since the
spectral function can be measured in experiments by the neutron scattering or
the angle-resolved photoemission spectroscopy(ARPES) technique, our equality
makes the fidelity susceptibility directly measurable in experiments.
Physically, our equality reveals also that the resonance peak in the spectral
function actually denotes a quantum criticality-like point at which the solid
state seemly undergoes a significant change.Comment: 5 pages, 2 figure
Scaling dimension of fidelity susceptibility in quantum phase transitions
We analyze ground-state behaviors of fidelity susceptibility (FS) and show
that the FS has its own distinct dimension instead of real system's dimension
in general quantum phases. The scaling relation of the FS in quantum phase
transitions (QPTs) is then established on more general grounds. Depending on
whether the FS's dimensions of two neighboring quantum phases are the same or
not, we are able to classify QPTs into two distinct types. For the latter type,
the change in the FS's dimension is a characteristic that separates two phases.
As a non-trivial application to the Kitaev honeycomb model, we find that the FS
is proportional to in the gapless phase, while in the gapped
phase. Therefore, the extra dimension of can be used as a
characteristic of the gapless phase.Comment: 4 pages, 1 figure, final version to appear in EP
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